Parkinson’s disease: an inquiry into the etiology and treatment

Parkinson’s disease affects over one million people in the United States. Although there have been remarkable advances in uncovering the pathogenesis of this disabling disorder, the etiology is speculative. Medical treatment and operative procedures provide symptomatic relief only. Compression of the cerebral peduncle of the midbrain by the posterior cerebral artery in a patient with Parkinson’s Disease (Parkinson’s Disease) was noted on magnetic resonance imaging (MRI) scan and at operation in a patient with trigeminal neuralgia. Following the vascular decompression of the trigeminal nerve, the midbrain was decompressed by mobilizing and repositioning the posterior cerebral artery The patient's Parkinson's signs disappeared over a 48-hour period. They returned 18 months later with contralateral peduncle compression. A blinded evaluation of MRI scans of Parkinson's patients and controls was performed. MRI scans in 20 Parkinson's patients and 20 age and sex matched controls were evaluated in blinded fashion looking for the presence and degree of arterial compression of the cerebral peduncle. The MRI study showed that 73.7 percent of Parkinson's Disease patients had visible arterial compression of the cerebral peduncle. This was seen in only 10 percent of control patients (two patients, one of whom subsequently developed Parkinson’s Disease); thus 5 percent. Vascular compression of the cerebral peduncle by the posterior cerebral artery may be associated with Parkinson’s Disease in some patients. Microva-scular decompression of that artery away from the peduncle may be considered for treatment of Parkinson’s Disease in some patients

Method selection and adaptation for distributed monitoring of infectious diseases for syndromic surveillance

AbstractBackgroundAutomated surveillance systems require statistical methods to recognize increases in visit counts that might indicate an outbreak. In prior work we presented methods to enhance the sensitivity of C2, a commonly used time series method. In this study, we compared the enhanced C2 method with five regression models.MethodsWe used emergency department chief complaint data from US CDC BioSense surveillance system, aggregated by city (total of 206 hospitals, 16 cities) during 5/2008–4/2009. Data for six syndromes (asthma, gastrointestinal, nausea and vomiting, rash, respiratory, and influenza-like illness) was used and was stratified by mean count (1–19, 20–49, ⩾50 per day) into 14 syndrome-count categories. We compared the sensitivity for detecting single-day artificially-added increases in syndrome counts. Four modifications of the C2 time series method, and five regression models (two linear and three Poisson), were tested. A constant alert rate of 1% was used for all methods.ResultsAmong the regression models tested, we found that a Poisson model controlling for the logarithm of total visits (i.e., visits both meeting and not meeting a syndrome definition), day of week, and 14-day time period was best. Among 14 syndrome-count categories, time series and regression methods produced approximately the same sensitivity (<5% difference) in 6; in six categories, the regression method had higher sensitivity (range 6–14% improvement), and in two categories the time series method had higher sensitivity.DiscussionWhen automated data are aggregated to the city level, a Poisson regression model that controls for total visits produces the best overall sensitivity for detecting artificially added visit counts. This improvement was achieved without increasing the alert rate, which was held constant at 1% for all methods. These findings will improve our ability to detect outbreaks in automated surveillance system data

Data acquisition process for an intelligent decision support in gynecology and obstetrics emergency triage

Manchester Triage System is a reliable system of triage in the emergency department of a hospital. This system when applied to a specific patients’ condition such the pregnancy has several limitations. To overcome those limitations an alternative triage IDSS was developed in the MJD. In this approach the knowledge was obtained directly from the doctors’ empirical and scientific experience to make the first version of decision models. Due to the particular gynecological and/or obstetrics requests other characteristics had been developed, namely a system that can increase patient safety for women in need of immediate care and help low-risk women avoid high-risk care, maximizing the use of resources. This paper presents the arrival flowchart, the associated decisions and the knowledge acquisition cycle. Results showed that this new approach enhances the efficiency and the safety through the appropriate use of resources and by assisting the right patient in the right place.The work of Filipe Portela was supported by the grant SFRH/BD/70156/2010 from FC

Accounting for seasonal patterns in syndromic surveillance data for outbreak detection

BACKGROUND: Syndromic surveillance (SS) can potentially contribute to outbreak detection capability by providing timely, novel data sources. One SS challenge is that some syndrome counts vary with season in a manner that is not identical from year to year. Our goal is to evaluate the impact of inconsistent seasonal effects on performance assessments (false and true positive rates) in the context of detecting anomalous counts in data that exhibit seasonal variation. METHODS: To evaluate the impact of inconsistent seasonal effects, we injected synthetic outbreaks into real data and into data simulated from each of two models fit to the same real data. Using real respiratory syndrome counts collected in an emergency department from 2/1/94–5/31/03, we varied the length of training data from one to eight years, applied a sequential test to the forecast errors arising from each of eight forecasting methods, and evaluated their detection probabilities (DP) on the basis of 1000 injected synthetic outbreaks. We did the same for each of two corresponding simulated data sets. The less realistic, nonhierarchical model's simulated data set assumed that "one season fits all," meaning that each year's seasonal peak has the same onset, duration, and magnitude. The more realistic simulated data set used a hierarchical model to capture violation of the "one season fits all" assumption. RESULTS: This experiment demonstrated optimistic bias in DP estimates for some of the methods when data simulated from the nonhierarchical model was used for DP estimation, thus suggesting that at least for some real data sets and methods, it is not adequate to assume that "one season fits all." CONCLUSION: For the data we analyze, the "one season fits all " assumption is violated, and DP performance claims based on simulated data that assume "one season fits all," for the forecast methods considered, except for moving average methods, tend to be optimistic. Moving average methods based on relatively short amounts of training data are competitive on all three data sets, but are particularly competitive on the real data and on data from the hierarchical model, which are the two data sets that violate the "one season fits all" assumption

Pediatric observation units in the United States: A systematic review

BACKGROUND: As more efficient and value-based care models are sought for the US healthcare system, geographically distinct observation units (OUs) may become an integral part of hospital-based care for children. PURPOSE: To systematically review the literature and evaluate the structure and function of pediatric OUs in the United States. DATA SOURCES: Searches were conducted in Medline, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Health Care Advisory Board (HCAB), Lexis-Nexis, National Guideline Clearinghouse, and Cochrane Reviews, through February 2009, with review of select bibliographies. STUDY SELECTION: English language peer-reviewed publications on pediatric OU care in the United States. DATA EXTRACTION: Two authors independently determined study eligibility. Studies were graded using a 5-level quality assessment tool. Data were extracted using a standardized form. DATA SYNTHESIS: A total of 21 studies met inclusion criteria: 2 randomized trials, 2 prospective observational, 12 retrospective cohort, 2 before and after, and 3 descriptive studies. Studies present data on more than 22,000 children cared for in OUs, most at large academic centers. This systematic review provides a descriptive overview of the structure and function of pediatric OUs in the United States. Despite seemingly straightforward outcomes for OU care, significant heterogeneity in the reporting of length of stay, admission rates, return visit rates, and costs precluded our ability to conduct meta-analyses. We propose standard outcome measures and future directions for pediatric OU research. CONCLUSIONS: Future research using consistent outcome measures will be critical to determining whether OUs can improve the quality and cost of providing care to children requiring observation-length stays. Journal of Hospital Medicine 2010;5:172–182. © 2010 Society of Hospital Medicine.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69173/1/592_ftp.pd

Improving access for community health and sub-acute outpatient services: protocol for a stepped wedge cluster randomised controlled trial

BACKGROUND: Waiting lists for treatment are common in outpatient and community services, Existing methods for managing access and triage to these services can lead to inequities in service delivery, inefficiencies and divert resources from frontline care. Evidence from two controlled studies indicates that an alternative to the traditional &quot;waitlist and triage&quot; model known as STAT (Specific Timely Appointments for Triage) may be successful in reducing waiting times without adversely affecting other aspects of patient care. This trial aims to test whether the model is cost effective in reducing waiting time across multiple services, and to measure the impact on service provision, health-related quality of life and patient satisfaction. METHODS/DESIGN: A stepped wedge cluster randomised controlled trial has been designed to evaluate the impact of the STAT model in 8 community health and outpatient services. The primary outcome will be waiting time from referral to first appointment. Secondary outcomes will be nature and quantity of service received (collected from all patients attending the service during the study period and health-related quality of life (AQOL-8D), patient satisfaction, health care utilisation and cost data (collected from a subgroup of patients at initial assessment and after 12&nbsp;weeks). Data will be analysed with a multiple multi-level random-effects regression model that allows for cluster effects. An economic evaluation will be undertaken alongside the clinical trial. DISCUSSION: This paper outlines the study protocol for a fully powered prospective stepped wedge cluster randomised controlled trial (SWCRCT) to establish whether the STAT model of access and triage can reduce waiting times applied across multiple settings, without increasing health service costs or adversely impacting on other aspects of patient care. If successful, it will provide evidence for the effectiveness of a practical model of access that can substantially reduce waiting time for outpatient and community services with subsequent benefits for both efficiency of health systems and patient care.<br /

Validation of Syndromic Surveillance for Respiratory Pathogen Activity

The studied respiratory syndromes are suitable for syndromic surveillance because they reflect respiratory pathogen activity pattern

Online detection and quantification of epidemics

<p>Abstract</p> <p>Background</p> <p>Time series data are increasingly available in health care, especially for the purpose of disease surveillance. The analysis of such data has long used periodic regression models to detect outbreaks and estimate epidemic burdens. However, implementation of the method may be difficult due to lack of statistical expertise. No dedicated tool is available to perform and guide analyses.</p> <p>Results</p> <p>We developed an online computer application allowing analysis of epidemiologic time series. The system is available online at <url>http://www.u707.jussieu.fr/periodic_regression/</url>. The data is assumed to consist of a periodic baseline level and irregularly occurring epidemics. The program allows estimating the periodic baseline level and associated upper forecast limit. The latter defines a threshold for epidemic detection. The burden of an epidemic is defined as the cumulated signal in excess of the baseline estimate. The user is guided through the necessary choices for analysis. We illustrate the usage of the online epidemic analysis tool with two examples: the retrospective detection and quantification of excess pneumonia and influenza (P&I) mortality, and the prospective surveillance of gastrointestinal disease (diarrhoea).</p> <p>Conclusion</p> <p>The online application allows easy detection of special events in an epidemiologic time series and quantification of excess mortality/morbidity as a change from baseline. It should be a valuable tool for field and public health practitioners.</p

Syndromic surveillance: STL for modeling, visualizing, and monitoring disease counts

<p>Abstract</p> <p>Background</p> <p>Public health surveillance is the monitoring of data to detect and quantify unusual health events. Monitoring pre-diagnostic data, such as emergency department (ED) patient chief complaints, enables rapid detection of disease outbreaks. There are many sources of variation in such data; statistical methods need to accurately model them as a basis for timely and accurate disease outbreak methods.</p> <p>Methods</p> <p>Our new methods for modeling daily chief complaint counts are based on a seasonal-trend decomposition procedure based on loess (STL) and were developed using data from the 76 EDs of the Indiana surveillance program from 2004 to 2008. Square root counts are decomposed into inter-annual, yearly-seasonal, day-of-the-week, and random-error components. Using this decomposition method, we develop a new synoptic-scale (days to weeks) outbreak detection method and carry out a simulation study to compare detection performance to four well-known methods for nine outbreak scenarios.</p> <p>Result</p> <p>The components of the STL decomposition reveal insights into the variability of the Indiana ED data. Day-of-the-week components tend to peak Sunday or Monday, fall steadily to a minimum Thursday or Friday, and then rise to the peak. Yearly-seasonal components show seasonal influenza, some with bimodal peaks.</p> <p>Some inter-annual components increase slightly due to increasing patient populations. A new outbreak detection method based on the decomposition modeling performs well with 90 days or more of data. Control limits were set empirically so that all methods had a specificity of 97%. STL had the largest sensitivity in all nine outbreak scenarios. The STL method also exhibited a well-behaved false positive rate when run on the data with no outbreaks injected.</p> <p>Conclusion</p> <p>The STL decomposition method for chief complaint counts leads to a rapid and accurate detection method for disease outbreaks, and requires only 90 days of historical data to be put into operation. The visualization tools that accompany the decomposition and outbreak methods provide much insight into patterns in the data, which is useful for surveillance operations.</p